#!/usr/bin/env bash
###########################################################
# Change the following values to train a new model.
# type: the name of the new model, only affects the saved file name.
# dataset: the name of the dataset, as was preprocessed using preprocess.sh
# test_data: by default, points to the validation set, since this is the set that
#   will be evaluated after each training iteration. If you wish to test
#   on the final (held-out) test set, change 'val' to 'test'.

#type=java14m
#dataset_name=java14m
#data_dir=data/${dataset_name}
#data=${data_dir}/${dataset_name}
#test_data=${data_dir}/${dataset_name}.val.c2v
#model_dir=models/${type}
#
#mkdir -p ${model_dir}
#set -e
#python3 -u code2vec.py --data ${data} --test ${test_data} --save ${model_dir}/saved_model

type=java-small
dataset_name=java-small
data_dir=F:\博士\看的论文\data\code2vec
data=${data_dir}/${dataset_name}.train.c2v
test_data=${data_dir}/${dataset_name}.test.c2v
model_dir=models/${type}

mkdir -p ${model_dir}
set -e
python3 -u code2vec.py --data ${data} --test ${test_data} --save ${model_dir}/saved_model
